摘要
采用具有良好稳定性和旋转不变性的奇异值作为匹配特征,根据奇异值的数值特点提出以加权距离作为相似性的度量;采用变模板分级匹配的策略,使得在进行大模板匹配时的匹配运算量大大降低·通过加入噪声、灰度变化和旋转变化的景像匹配实验,证实了该算法的有效性和鲁棒性·
Novel image matching method based on singular value decomposition （SVD） is proposed in the paper, by taking into account the features of singular values＇ stability and rotation-invariance. Based on singular values＇ unique characteristics, this paper further proposes to use weighted distance as the simlarity measure between images. In addition, a new coarse-to-fine hierarchical matching approach is proposed to significantly reduce the computation cost. The proposed algorithm has been tested on various corrupted images including noise addition, intensity change and rotation change. Experimental results demonstrate the robustness and accuracy of the proposed algorithm.
出处
《计算机辅助设计与图形学学报》
EI
CSCD
北大核心
2006年第2期212-216,共5页
Journal of Computer-Aided Design & Computer Graphics
基金
航空科学基金(01C15001)
关键词
景像匹配
奇异值分解
变模板匹配
scene matching
singular value decomposition
hierarchical multi-mask matching
